Multiplicative-error models with sample selection

Abstract: This paper presents simple approaches to deal with sample selection in models with multiplicative errors. GMM estimators are constructed for both cross-section data and for panel data. These estimators build only on a specification of the conditional mean of the outcome of interest and are, therefore, semiparametric in nature. In particular, the distribution of unobservables is left unspecified. In the panel-data case, we further allow for group-specific fixed effects whose relation to covariates is left unrestricted. We derive distribution theory for both sampling situations and present Monte Carlo evidence on the finite-sample performance of the approach.

Keywords: nonlinear model; sample selection; semiparametric inference; two-stage estimation (search for similar items in EconPapers)
Date: 2014-05
Note: View the original document on HAL open archive server: https://sciencespo.hal.science/hal-00987290
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Related works:
Journal Article: Multiplicative-error models with sample selection (2015)
Working Paper: Multiplicative-error models with sample selection (2015)
Working Paper: Multiplicative-error models with sample selection (2015)
Working Paper: Multiplicative-error models with sample selection (2014)
Working Paper: Multiplicative-error models with sample selection (2014)
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